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4 MA Strategy + Position Management// This is a simple crossover Moving Average strategy, good for long term crypto trades. // It buys when the MA "X" crosses up the MA "Y", viceversa for shorts. // Both MAs are selectable from the Inputs section in the front panel. // There is also a Position Management option thats // sizes positions to have the same USD risk (using leverage) on each trade, // based on the percentage distance to the stop loss level. // If you turn this option on you will see how the profit // grows exponentially while the drawdown percentage almost remains the same.
Strategia Pine Script®
di tweakerID
1
Volume, Momentum and Volatility weighted moving averageMoving averages are filters on price data. This moving average creates a filter which factors in: - the price RSI or it's Momentum - the volume RSI - the RVI or Volatility Each factor is put through a least squares filter to smooth them first. Then the factors are used to build a coefficient for an exponentially weighted average. The chart above shows a comparison of standard average types with this script. This is useful if you are looking for a moving average based trigger and do not wish to react to candle noise price action.
Indicatore Pine Script®
di JohnBaron
6
Linear Regression + Moving Average1. Linear Regression including 2 x Standard Deviation + High / Low. Middle line colour depends on colour change of Symmetrically Weighted Moving Average . Green zones indicate good long positions. Red zones indicate good short positions. (Custom) 2. Symmetrically Weighted Moving Average. Colour change depending on cross of offset -1. (Fixed) 3. Exponentially Weighted Moving Average. Colour change depending on cross with Symmetrically Weighted Moving Average. (Custom)
Indicatore Pine Script®
di Amstelveen
6
Volume Weighted Directional BiasThis indicator uses a series of five volume weighted moving averages cast out in successive powers of three to calculate a value which expresses the direction and momentum of a trend. It can be used as a contrary indicator to identify waning momentum at the top or bottom of a rally or selloff. It can be used to identify trendline divergence. It can also be used for trend confirmation. The length of the moving averages can be changed in the indicator inputs, but each should be longer than the previous. The problem with most trend indicators is that they are either too lagging or too noisy. This indicator seeks to combine smoothed data and a long lookback period with an exponentially forward weighted calculation, making it still very responsive to market changes without too much signal noise.
Indicatore Pine Script®
di iamroot
3
Grid ToolThe core idea of this grid tool is that you have to concentrate less on the trade entries (this happens automatically time-independent but price-dependant) but rather on the validity of the macro trend. Exiting a trend when it is no longer valid is more important than entering a trade. But as long as the trend is valid, the trader participates exponentially in the overall trend. It is advisable to start with a basic position and then "set up" the grid on this in a ratio of 1/10. A major advantage of grid trading is that the average entry price in a trend moves further and further away from the current market price while the position continues to grow. A small timeframe should be used so that the distance between the trades corresponds as closely as possible to the selected grid gap and since TV backtests are carried out with closed bars. Before starting a grid, pre-analysis the market to make sure it is trending. Select the grid gap and grid position size that you are comfortable with. Monitor the trend and from time to time take some profit :). PS: The ADX filter looks interesting.
Strategia Pine Script®
di ramsay09
6
[SCL] Bitcoin Hashrate Dips (Miner Capitulation)Gives long-term buy signals for Bitcoin from dips and recoveries in the hashrate (the "miner capitulation" theory). It has an overlay and a standalone mode and is fully configurable. It uses Williams Lows and ATR instead of moving averages. Features that might be interesting for Pinescripters: + Automatic (as far as is possible currently) adjustment of plots for overlay and standalone display modes. + A neat label function for debugging floats + Fully commented + The debug that I used to overcome problems in developing it is left in + Ideas for how to deal with a wildly oscillating and exponentially increasing data source You need to run this indicator on the Bitcoin daily chart for it to make any sense. The best is a BTC/USD chart with a long history, such as BNC:BLX.
Indicatore Pine Script®
di SimpleCryptoLife
9
KINSKI Flexible Multi MA (EMA, SMA, RMA, WMA, VWMA, KAMA, HMA)This Multi Moving Average (MA) indicator is more flexible than any other indicator of this type offered so far. You can define up to 10 different Moving Average (MA) lines based on different calculation variants. The following MA types can be configured. - EMA: Exponentially Moving Average - SMA: Small Moving Average - RMA: Rolling Moving Average - WMA: Weighted Moving Average - VWMA: Volume Weighted Moving Average - KAMA: Kaufman's Adaptive Moving Average - HMA: Hull Moving Average Which settings can be made? - Selection for calculation formula ("Calculation Source"). The default value is "close". - for each MA line the "Length" and the "Type" can be defined - furthermore you can make layout adjustments via the "Style" menu
Indicatore Pine Script®
di KINSKI
56
Dynamic Money FlowDynamic Money Flow is a volume indicator based on Marc Chaikin's Money Flow with a few improvements. It can be used to confirm break-outs and trends. Zero line crosses and divergences can provide useful signals while considering chart analysis as well. Two weaknesses of CMF have been already fixed by Colin Twiggs (IncredibleCharts)... 1.CMF uses Chaikin's accumulation/distribution line to calculate the flow of money. Accumulation/distribution line does not take the gaps into account. This can be solved using true range. I call it true accumulation/distribution. 2.Oscillators have a tendency to center because of averaging calculations. DMF is average of flowing volume divided by average of total volume. This means indicator plots the change of first factor compared to the other one. In Simple Averaging method every data is given an equal weight thus when the last data drops it will have heavy impact on the averages and the change of them. It is much easier to identity these impacts after the drop of very high or very low data... So reducing the weight exponentially is a better option. 3.There is something else with CMF... changes of close price is ignored, because the formula only compares close price to its range. To include the movements of close beside the close to range comparison, the distance between two last close prices should be compared to true range as well. So volume can be distributed between close to range comparison (True Accumulation/Distribution) and close to close comparison automatically. And then results are summed to have a single multiplier. An example for how close to close comparison affects DMF... Or here you can see how lower wicks keep TMF (same as CMF in this case) from crossing zero line while price is trending down.
Indicatore Pine Script®
di RezzaHmt
44
Bitmex Funding Killzones v3 [MaliciousUpload]Originally built off of "Oscarvs: BITCOIN KILL ZONES v2" indicator, updated to now highlight a different time period based event. 1. The indicator should not be affected by what time zone you are in, it will show true Funding periods by default. 2. This needs to be used on the 1min time frame to be used to its full extent 3. The more the funding fee is the more likely you are to have price get manipulated by people looking to act on its benefit 4. This indicator will work only for XBTUSD and ETHUSD perpetual contract symbols as they are the only two ones with funding... My opinion: Funding is literally the exchanges insurance policy, they are "the house", they will always win. With that in mind you can trade "with the house" in this regard, getting onto the side that will benefit from exponentially large funding rebates. Do you ever ask how those "whales" got to be rich? It was by saving every penny they could while trading. Funding gives people the option to jump out right before, avoiding the fee and then immediately enter in after at no loss (assuming limit orders ofc). If that doesn't make sense to you i cant help, sorry. :pray: :pray: :pray: "Build up period" = Usually when we see people start getting into positions to try and get the rebate from funding and/or people getting out of positions that would be negatively impacted by funding "Entry window" = If you are trying to scalp the "rebound" in price which should happen right after funding happens from people re-entering their position which previously exited just to avoid the funding fee or from all of the people who entered just to get the funding rebate "Take profit period" = The time period I have determined to be most influential, very volatile IF the funding has an effect on price Hit me up on Discord if you are an **experienced** trader that takes trading seriously. MaliciousUpload#1637
Indicatore Pine Script®
di AlphaBotSystem
3
WhenWasThePriceAction Bars of largest range (volatility) * see moments of strongest price action immediately * colored & upDown by candle color * amplifier: you see only the bull runs, and subsequent dumps Very nice on the 5 years scale of BITSTAMP:BTCUSD - nothing comparable to 2013 has happened yet. Internals: squared_range = pow(high-low, 2) That is essentially it already. The rest are details: * gauge with (in case of Bitcoin exponentially rising) price * show in red for negative candles * take even higher polynomial (than 2) to show only the very largest values * allow some user input (but there is not much more that can be chosen here.) Sorry for such a simple formula - but sometimes the easiest things are powerful. Please give feedback. www.tradingview.com and/or in the cryptocurrency chat. Thanks.
Indicatore Pine Script®
di akd
3
FREE INDICATOR: POLARIZED FRACTAL EFFICIENCYLooking for something other than a moving average to help determine not only a trend's strength, but also it's direction? Try PFE! PFE was developed by Hans Hannula that was invented to determine price efficiency over a user-defined time period. The Polarized Fractal Efficiency indicator is, in the essence, an exponentially smoothed ratio of the length of two lines: (1) of a straight line between today’s close and the close Period days ago, and (2) of a broken line connecting all Close points between today and Period days ago. The indicator output varies between -100 and 100. The theory behind this indicator is that if it is >50 (or <-50) then the market is likely to reverse its trend from positive to negative (or from negative to positive). Other usage: Securities with a PFE greater than zero are deemed to be trending up, while a reading of less than zero indicates the trend is down. The strengh of the trend is measured by the position of the PFE relative to the zero line. As a general rule, the further the PFE value is away from zero, the stronger and more efficient the given trend is. A PFE value that fluctuates around the zero line could indicate that the supply and demand for the security are in balance and price may trade sideways. As with all indicators, finding something that works well along side this would be the most beneficial way to use it. Perhaps something like the Choppiness Index (related idea below) could do the trick. Grab the source code here: pastebin.com Installation video by @ChrisMoody here : blog.tradingview.com
Indicatore Pine Script®
di TheLark
3
My_EMA_CloudsThis script is a comprehensive technical indicator for trading, which includes several functional blocks: Consolidation zones Detects and displays price consolidation areas Draws horizontal support/resistance lines Generates breakout alerts (up/down) Allows customization of analysis period and minimum consolidation length EMA Clouds (Exponential Moving Averages) Contains 5 sets of EMA clouds with customizable periods Each cloud consists of short and long EMAs Cloud colors change depending on trend direction Offers offset and display settings customization Support and Resistance Levels Automatically detects key levels Uses ATR (Average True Range) for calculation Displays extended levels Allows visual style customization Side Volume Indicator Shows volume distribution across price levels Visualizes buy and sell volumes Displays Point of Control (PoC) Customizable number of histograms Liquidation Zones Identifies potential areas of mass position liquidations Displays levels with different multipliers (10x, 25x, 50x, 100x) Shows position volume Includes heatmap functionality The script provides traders with a comprehensive set of tools for market analysis, including trend indicators, support/resistance levels, volume metrics, and potential price movement zones. All components can be customized to fit individual trading strategies. Best usage with Likelihood of Winning - Probability Density Function Данный скрипт представляет собой комплексный технический индикатор для трейдинга, который включает в себя несколько функциональных блоков: Зоны консолидации Определяет и отображает области консолидации цены Рисует горизонтальные линии поддержки/сопротивления Генерирует оповещения о прорывах вверх/вниз Позволяет настраивать период анализа и минимальную длину консолидации Облака EMA (Exponential Moving Averages) Содержит 5 наборов EMA-облаков с настраиваемыми периодами Каждое облако состоит из короткой и длинной EMA Цвета облаков меняются в зависимости от направления тренда Есть возможность настройки смещения и отображения Уровни поддержки и сопротивления Автоматически определяет ключевые уровни Использует ATR (средний истинный диапазон) для расчета Отображает расширенные уровни Позволяет настраивать визуальный стиль Индикатор бокового объема Показывает распределение объема по ценовым уровням Визуализирует объемы покупок и продаж Отображает точку контроля (PoC) Настраиваемое количество гистограмм Зоны ликвидаций Определяет потенциальные зоны массовых ликвидаций позиций Отображает уровни с разными множителями (10x, 25x, 50x, 100x) Показывает объем позиций Включает функцию тепловой карты Скрипт предоставляет трейдерам комплексный набор инструментов для анализа рынка, включая трендовые индикаторы, уровни поддержки/сопротивления, объемные показатели и зоны потенциальных движений цены. Все компоненты можно настраивать под индивидуальные торговые стратегии.
Indicatore Pine Script®
di vslvnvch
EMA vs TMA Regime FilterEMA vs TMA Regime Filter This indicator is built as a visual study tool to compare the behavior of the Exponential Moving Average (EMA) and the Triangular Moving Average (TMA). The EMA applies an exponential weighting to price data, giving stronger importance to the most recent values. This makes it a faster, more responsive line that reflects short-term momentum. The TMA, by contrast, applies a double-smoothing process (or in the “True TMA” option, a split SMA sequence), which produces a much slower curve. The TMA emphasizes balance over reactivity, often used for filtering noise and observing longer-term structure. When both are plotted on the same chart, their differences become clear. The shaded region between them highlights times when short-term price dynamics diverge from longer-term smoothing. This is where the idea of “regime” comes in — not as a trading signal, but as a descriptive way of seeing whether market action is currently dominated by speed or by stability. Users can customize: Line styles, widths, and colors. Cloud transparency for visual clarity. Whether to color bars based on relative position (optional, purely visual). The goal is not to create a system, but to help traders experiment, observe, and learn how different smoothing techniques can emphasize different aspects of price. By switching between the legacy and true TMA, or adjusting lengths, users can study how each approach interprets the same data differently.
Indicatore Pine Script®
di geneclash
On-Balance Volume with Multiple MA TypesOn-Balance Volume with Multiple MA Types English Description Overview This is the first version of the "On-Balance Volume with Multiple MA Types" indicator designed to overlay directly on the price chart, a significant evolution from its previous iterations, which functioned solely as an oscillator in a separate window. The indicator calculates On-Balance Volume (OBV) and applies various smoothing methods to provide a clear view of volume dynamics in relation to price movements. It is pinned to the price scale for seamless integration with the chart. Interpretation Recommendations Price Pushing the OBV Line from Below: When the price chart pushes the OBV line upward and remains below it, this indicates rising volume, suggesting strong buying pressure. Price Above the OBV Line: When the price chart is above the OBV line, it signals falling volume, indicating weakening momentum or selling pressure. OBV Line Crossings: When the price crosses the OBV line, it represents a balance point in volume dynamics. The price level at the current crossing can be compared to the previous crossing to assess changes in market sentiment or momentum. Moving Average Types The indicator offers eight smoothing options for the OBV line, each with unique characteristics: EMA (Exponential Moving Average): A weighted average that prioritizes recent data, providing a smooth yet responsive line. DEMA (Double Exponential Moving Average): Uses two EMAs to reduce lag, offering faster response to volume changes. HMA (Hull Moving Average): Combines weighted moving averages to minimize lag while maintaining smoothness. WMA (Weighted Moving Average): Assigns more weight to recent data, balancing responsiveness and noise reduction. TMA (Triangular Moving Average): A double-smoothed simple moving average, emphasizing central data points for smoother output. VIDYA (Variable Index Dynamic Average): Adapts smoothing based on market volatility, using a CMO (Chande Momentum Oscillator) for dynamic weighting. Controlled by the VIDYA Alpha parameter (default: 0.2, range: 0–1), which adjusts sensitivity to volatility. FRAMA (Fractal Adaptive Moving Average): Adjusts smoothing based on fractal dimensions of the OBV data, adapting to market conditions. JMA (Jurik Moving Average): A proprietary adaptive average designed for minimal lag and high smoothness. Controlled by two parameters: JMA Phase (default: 50, range: -100 to 100): Adjusts the balance between responsiveness and smoothness. JMA Power (default: 1, range: 0.1+): Controls the strength of smoothing. Input Parameters OBV MA Length (default: 10): The lookback period for smoothing the OBV. Higher values produce smoother results but increase lag. OBV MA Type (default: JMA): Selects the moving average type from the eight options listed above. Line Width (default: 2): Thickness of the OBV line on the chart. Bullish Color (default: Blue): Color of the OBV line when rising (indicating increasing volume). Bearish Color (default: Red): Color of the OBV line when falling (indicating decreasing volume). JMA Phase (default: 50): Adjusts the JMA’s responsiveness (used only when JMA is selected). JMA Power (default: 1): Adjusts the JMA’s smoothing strength (used only when JMA is selected). VIDYA Alpha (default: 0.2): Controls the sensitivity of VIDYA to market volatility (used only when VIDYA is selected). How to Use Add the indicator to your TradingView chart. It will overlay directly on the price chart, aligned with the price scale. Adjust the OBV MA Type to select your preferred smoothing method based on your trading style (e.g., JMA for low lag, TMA for smoothness). Modify the OBV MA Length to balance responsiveness and noise reduction. Shorter periods (e.g., 5–10) are better for short-term trading, while longer periods (e.g., 20–50) suit longer-term analysis. Use the Bullish Color and Bearish Color to visually distinguish rising and falling volume trends. For JMA or VIDYA, fine-tune the JMA Phase, JMA Power, or VIDYA Alpha to optimize the indicator for specific market conditions. Interpret the OBV line in relation to price: Watch for price pushing the OBV line upward (rising volume) or moving above it (falling volume). Note crossings of the OBV line to identify balance points and compare with prior crossings to gauge momentum shifts. Combine with other technical tools (e.g., support/resistance levels, trendlines) for a comprehensive trading strategy. Notes This indicator is designed to work on any timeframe and market, but its effectiveness depends on the chosen moving average type and parameters. Experiment with different MA types and lengths to find the best fit for your trading approach. The indicator is licensed under the Mozilla Public License 2.0 and copyrighted by TradingStrategyCourses © 2025.
Indicatore Pine Script®
di SYNC-AND-TRADE
Trend CandlesTrend Candles Overview The Trend Candles indicator is a simple yet effective tool designed to help traders visually identify the prevailing market trend. By combining candle coloring with a trend-based Exponential Moving Average (EMA), it enhances chart readability and makes trend-following strategies easier to apply. Concepts Exponential Moving Average (EMA): The EMA is a moving average that places more weight on recent price data. It reacts faster to price changes compared to a Simple Moving Average (SMA), making it well-suited for trend detection. Trend Determination: - If the EMA is rising (current EMA > previous EMA), the market is considered bullish. - If the EMA is falling (current EMA < previous EMA), the market is considered bearish. - If the EMA is flat (no significant change), no trend color is applied. Candle Coloring: - Green candles = Uptrend - Purple candles = Downtrend - Default candles = Sideways/Flat EMA Features - Trend Visualization: Candles automatically change color based on EMA slope, making it easy to spot bullish and bearish phases. - Customizable EMA Length: The trader can set the EMA period (default is 50), allowing flexibility for short-term or long-term trend analysis. - Overlay EMA Line: An orange EMA line is plotted on the chart for additional confirmation of the trend. - Clean & Minimalist: Focuses on trend clarity without cluttering the chart with unnecessary signals. How to Use 1. Apply the indicator to your chart. 2. Adjust the EMA Length as per your trading style (shorter = faster signals, longer = smoother trend). 3. Follow the candle color: - Green = Favor long entries. - Purple = Favor short entries. - No color = Stay cautious, as trend is unclear. 4. Use with other confirmation tools (support/resistance, volume, or oscillators). 5. Users are encouraged to experiment with different EMA lengths. The default length is 50, but you can explore other values based on your needs. In particular, try Fibonacci numbers such as 13, 21, 34, 55, 89, 144, and 233 to observe how trends behave differently. Disclaimer The information provided by the Trend Candles indicator is for educational purposes only. It should not be considered financial advice. Trading involves substantial risk, and past performance is not necessarily indicative of future results. Always do your own research and use risk management practices.
Indicatore Pine Script®
di moneypots
Markov Chain [3D] | FractalystWhat exactly is a Markov Chain? This indicator uses a Markov Chain model to analyze, quantify, and visualize the transitions between market regimes (Bull, Bear, Neutral) on your chart. It dynamically detects these regimes in real-time, calculates transition probabilities, and displays them as animated 3D spheres and arrows, giving traders intuitive insight into current and future market conditions. How does a Markov Chain work, and how should I read this spheres-and-arrows diagram? Think of three weather modes: Sunny, Rainy, Cloudy. Each sphere is one mode. The loop on a sphere means “stay the same next step” (e.g., Sunny again tomorrow). The arrows leaving a sphere show where things usually go next if they change (e.g., Sunny moving to Cloudy). Some paths matter more than others. A more prominent loop means the current mode tends to persist. A more prominent outgoing arrow means a change to that destination is the usual next step. Direction isn’t symmetric: moving Sunny→Cloudy can behave differently than Cloudy→Sunny. Now relabel the spheres to markets: Bull, Bear, Neutral. Spheres: market regimes (uptrend, downtrend, range). Self‑loop: tendency for the current regime to continue on the next bar. Arrows: the most common next regime if a switch happens. How to read: Start at the sphere that matches current bar state. If the loop stands out, expect continuation. If one outgoing path stands out, that switch is the typical next step. Opposite directions can differ (Bear→Neutral doesn’t have to match Neutral→Bear). What states and transitions are shown? The three market states visualized are: Bullish (Bull): Upward or strong-market regime. Bearish (Bear): Downward or weak-market regime. Neutral: Sideways or range-bound regime. Bidirectional animated arrows and probability labels show how likely the market is to move from one regime to another (e.g., Bull → Bear or Neutral → Bull). How does the regime detection system work? You can use either built-in price returns (based on adaptive Z-score normalization) or supply three custom indicators (such as volume, oscillators, etc.). Values are statistically normalized (Z-scored) over a configurable lookback period. The normalized outputs are classified into Bull, Bear, or Neutral zones. If using three indicators, their regime signals are averaged and smoothed for robustness. How are transition probabilities calculated? On every confirmed bar, the algorithm tracks the sequence of detected market states, then builds a rolling window of transitions. The code maintains a transition count matrix for all regime pairs (e.g., Bull → Bear). Transition probabilities are extracted for each possible state change using Laplace smoothing for numerical stability, and frequently updated in real-time. What is unique about the visualization? 3D animated spheres represent each regime and change visually when active. Animated, bidirectional arrows reveal transition probabilities and allow you to see both dominant and less likely regime flows. Particles (moving dots) animate along the arrows, enhancing the perception of regime flow direction and speed. All elements dynamically update with each new price bar, providing a live market map in an intuitive, engaging format. Can I use custom indicators for regime classification? Yes! Enable the "Custom Indicators" switch and select any three chart series as inputs. These will be normalized and combined (each with equal weight), broadening the regime classification beyond just price-based movement. What does the “Lookback Period” control? Lookback Period (default: 100) sets how much historical data builds the probability matrix. Shorter periods adapt faster to regime changes but may be noisier. Longer periods are more stable but slower to adapt. How is this different from a Hidden Markov Model (HMM)? It sets the window for both regime detection and probability calculations. Lower values make the system more reactive, but potentially noisier. Higher values smooth estimates and make the system more robust. How is this Markov Chain different from a Hidden Markov Model (HMM)? Markov Chain (as here): All market regimes (Bull, Bear, Neutral) are directly observable on the chart. The transition matrix is built from actual detected regimes, keeping the model simple and interpretable. Hidden Markov Model: The actual regimes are unobservable ("hidden") and must be inferred from market output or indicator "emissions" using statistical learning algorithms. HMMs are more complex, can capture more subtle structure, but are harder to visualize and require additional machine learning steps for training. A standard Markov Chain models transitions between observable states using a simple transition matrix, while a Hidden Markov Model assumes the true states are hidden (latent) and must be inferred from observable “emissions” like price or volume data. In practical terms, a Markov Chain is transparent and easier to implement and interpret; an HMM is more expressive but requires statistical inference to estimate hidden states from data. Markov Chain: states are observable; you directly count or estimate transition probabilities between visible states. This makes it simpler, faster, and easier to validate and tune. HMM: states are hidden; you only observe emissions generated by those latent states. Learning involves machine learning/statistical algorithms (commonly Baum–Welch/EM for training and Viterbi for decoding) to infer both the transition dynamics and the most likely hidden state sequence from data. How does the indicator avoid “repainting” or look-ahead bias? All regime changes and matrix updates happen only on confirmed (closed) bars, so no future data is leaked, ensuring reliable real-time operation. Are there practical tuning tips? Tune the Lookback Period for your asset/timeframe: shorter for fast markets, longer for stability. Use custom indicators if your asset has unique regime drivers. Watch for rapid changes in transition probabilities as early warning of a possible regime shift. Who is this indicator for? Quants and quantitative researchers exploring probabilistic market modeling, especially those interested in regime-switching dynamics and Markov models. Programmers and system developers who need a probabilistic regime filter for systematic and algorithmic backtesting: The Markov Chain indicator is ideally suited for programmatic integration via its bias output (1 = Bull, 0 = Neutral, -1 = Bear). Although the visualization is engaging, the core output is designed for automated, rules-based workflows—not for discretionary/manual trading decisions. Developers can connect the indicator’s output directly to their Pine Script logic (using input.source()), allowing rapid and robust backtesting of regime-based strategies. It acts as a plug-and-play regime filter: simply plug the bias output into your entry/exit logic, and you have a scientifically robust, probabilistically-derived signal for filtering, timing, position sizing, or risk regimes. The MC's output is intentionally "trinary" (1/0/-1), focusing on clear regime states for unambiguous decision-making in code. If you require nuanced, multi-probability or soft-label state vectors, consider expanding the indicator or stacking it with a probability-weighted logic layer in your scripting. Because it avoids subjectivity, this approach is optimal for systematic quants, algo developers building backtested, repeatable strategies based on probabilistic regime analysis. What's the mathematical foundation behind this? The mathematical foundation behind this Markov Chain indicator—and probabilistic regime detection in finance—draws from two principal models: the (standard) Markov Chain and the Hidden Markov Model (HMM). How to use this indicator programmatically? The Markov Chain indicator automatically exports a bias value (+1 for Bullish, -1 for Bearish, 0 for Neutral) as a plot visible in the Data Window. This allows you to integrate its regime signal into your own scripts and strategies for backtesting, automation, or live trading. Step-by-Step Integration with Pine Script (input.source) Add the Markov Chain indicator to your chart. This must be done first, since your custom script will "pull" the bias signal from the indicator's plot. In your strategy, create an input using input.source() Example: //@version=5 strategy("MC Bias Strategy Example") mcBias = input.source(close, "MC Bias Source") After saving, go to your script’s settings. For the “MC Bias Source” input, select the plot/output of the Markov Chain indicator (typically its bias plot). Use the bias in your trading logic Example (long only on Bull, flat otherwise): if mcBias == 1 strategy.entry("Long", strategy.long) else strategy.close("Long") For more advanced workflows, combine mcBias with additional filters or trailing stops. How does this work behind-the-scenes? TradingView’s input.source() lets you use any plot from another indicator as a real-time, “live” data feed in your own script (source). The selected bias signal is available to your Pine code as a variable, enabling logical decisions based on regime (trend-following, mean-reversion, etc.). This enables powerful strategy modularity : decouple regime detection from entry/exit logic, allowing fast experimentation without rewriting core signal code. Integrating 45+ Indicators with Your Markov Chain — How & Why The Enhanced Custom Indicators Export script exports a massive suite of over 45 technical indicators—ranging from classic momentum (RSI, MACD, Stochastic, etc.) to trend, volume, volatility, and oscillator tools—all pre-calculated, centered/scaled, and available as plots. // Enhanced Custom Indicators Export - 45 Technical Indicators // Comprehensive technical analysis suite for advanced market regime detection //@version=6 indicator('Enhanced Custom Indicators Export | Fractalyst', shorttitle='Enhanced CI Export', overlay=false, scale=scale.right, max_labels_count=500, max_lines_count=500) // |----- Input Parameters -----| // momentum_group = "Momentum Indicators" trend_group = "Trend Indicators" volume_group = "Volume Indicators" volatility_group = "Volatility Indicators" oscillator_group = "Oscillator Indicators" display_group = "Display Settings" // Common lengths length_14 = input.int(14, "Standard Length (14)", minval=1, maxval=100, group=momentum_group) length_20 = input.int(20, "Medium Length (20)", minval=1, maxval=200, group=trend_group) length_50 = input.int(50, "Long Length (50)", minval=1, maxval=200, group=trend_group) // Display options show_table = input.bool(true, "Show Values Table", group=display_group) table_size = input.string("Small", "Table Size", options= , group=display_group) // |----- MOMENTUM INDICATORS (15 indicators) -----| // // 1. RSI (Relative Strength Index) rsi_14 = ta.rsi(close, length_14) rsi_centered = rsi_14 - 50 // 2. Stochastic Oscillator stoch_k = ta.stoch(close, high, low, length_14) stoch_d = ta.sma(stoch_k, 3) stoch_centered = stoch_k - 50 // 3. Williams %R williams_r = ta.stoch(close, high, low, length_14) - 100 // 4. MACD (Moving Average Convergence Divergence) = ta.macd(close, 12, 26, 9) // 5. Momentum (Rate of Change) momentum = ta.mom(close, length_14) momentum_pct = (momentum / close ) * 100 // 6. Rate of Change (ROC) roc = ta.roc(close, length_14) // 7. Commodity Channel Index (CCI) cci = ta.cci(close, length_20) // 8. Money Flow Index (MFI) mfi = ta.mfi(close, length_14) mfi_centered = mfi - 50 // 9. Awesome Oscillator (AO) ao = ta.sma(hl2, 5) - ta.sma(hl2, 34) // 10. Accelerator Oscillator (AC) ac = ao - ta.sma(ao, 5) // 11. Chande Momentum Oscillator (CMO) cmo = ta.cmo(close, length_14) // 12. Detrended Price Oscillator (DPO) dpo = close - ta.sma(close, length_20) // 13. Price Oscillator (PPO) ppo = ta.sma(close, 12) - ta.sma(close, 26) ppo_pct = (ppo / ta.sma(close, 26)) * 100 // 14. TRIX trix_ema1 = ta.ema(close, length_14) trix_ema2 = ta.ema(trix_ema1, length_14) trix_ema3 = ta.ema(trix_ema2, length_14) trix = ta.roc(trix_ema3, 1) * 10000 // 15. Klinger Oscillator klinger = ta.ema(volume * (high + low + close) / 3, 34) - ta.ema(volume * (high + low + close) / 3, 55) // 16. Fisher Transform fisher_hl2 = 0.5 * (hl2 - ta.lowest(hl2, 10)) / (ta.highest(hl2, 10) - ta.lowest(hl2, 10)) - 0.25 fisher = 0.5 * math.log((1 + fisher_hl2) / (1 - fisher_hl2)) // 17. Stochastic RSI stoch_rsi = ta.stoch(rsi_14, rsi_14, rsi_14, length_14) stoch_rsi_centered = stoch_rsi - 50 // 18. Relative Vigor Index (RVI) rvi_num = ta.swma(close - open) rvi_den = ta.swma(high - low) rvi = rvi_den != 0 ? rvi_num / rvi_den : 0 // 19. Balance of Power (BOP) bop = (close - open) / (high - low) // |----- TREND INDICATORS (10 indicators) -----| // // 20. Simple Moving Average Momentum sma_20 = ta.sma(close, length_20) sma_momentum = ((close - sma_20) / sma_20) * 100 // 21. Exponential Moving Average Momentum ema_20 = ta.ema(close, length_20) ema_momentum = ((close - ema_20) / ema_20) * 100 // 22. Parabolic SAR sar = ta.sar(0.02, 0.02, 0.2) sar_trend = close > sar ? 1 : -1 // 23. Linear Regression Slope lr_slope = ta.linreg(close, length_20, 0) - ta.linreg(close, length_20, 1) // 24. Moving Average Convergence (MAC) mac = ta.sma(close, 10) - ta.sma(close, 30) // 25. Trend Intensity Index (TII) tii_sum = 0.0 for i = 1 to length_20 tii_sum += close > close ? 1 : 0 tii = (tii_sum / length_20) * 100 // 26. Ichimoku Cloud Components ichimoku_tenkan = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2 ichimoku_kijun = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2 ichimoku_signal = ichimoku_tenkan > ichimoku_kijun ? 1 : -1 // 27. MESA Adaptive Moving Average (MAMA) mama_alpha = 2.0 / (length_20 + 1) mama = ta.ema(close, length_20) mama_momentum = ((close - mama) / mama) * 100 // 28. Zero Lag Exponential Moving Average (ZLEMA) zlema_lag = math.round((length_20 - 1) / 2) zlema_data = close + (close - close ) zlema = ta.ema(zlema_data, length_20) zlema_momentum = ((close - zlema) / zlema) * 100 // |----- VOLUME INDICATORS (6 indicators) -----| // // 29. On-Balance Volume (OBV) obv = ta.obv // 30. Volume Rate of Change (VROC) vroc = ta.roc(volume, length_14) // 31. Price Volume Trend (PVT) pvt = ta.pvt // 32. Negative Volume Index (NVI) nvi = 0.0 nvi := volume < volume ? nvi + ((close - close ) / close ) * nvi : nvi // 33. Positive Volume Index (PVI) pvi = 0.0 pvi := volume > volume ? pvi + ((close - close ) / close ) * pvi : pvi // 34. Volume Oscillator vol_osc = ta.sma(volume, 5) - ta.sma(volume, 10) // 35. Ease of Movement (EOM) eom_distance = high - low eom_box_height = volume / 1000000 eom = eom_box_height != 0 ? eom_distance / eom_box_height : 0 eom_sma = ta.sma(eom, length_14) // 36. Force Index force_index = volume * (close - close ) force_index_sma = ta.sma(force_index, length_14) // |----- VOLATILITY INDICATORS (10 indicators) -----| // // 37. Average True Range (ATR) atr = ta.atr(length_14) atr_pct = (atr / close) * 100 // 38. Bollinger Bands Position bb_basis = ta.sma(close, length_20) bb_dev = 2.0 * ta.stdev(close, length_20) bb_upper = bb_basis + bb_dev bb_lower = bb_basis - bb_dev bb_position = bb_dev != 0 ? (close - bb_basis) / bb_dev : 0 bb_width = bb_dev != 0 ? (bb_upper - bb_lower) / bb_basis * 100 : 0 // 39. Keltner Channels Position kc_basis = ta.ema(close, length_20) kc_range = ta.ema(ta.tr, length_20) kc_upper = kc_basis + (2.0 * kc_range) kc_lower = kc_basis - (2.0 * kc_range) kc_position = kc_range != 0 ? (close - kc_basis) / kc_range : 0 // 40. Donchian Channels Position dc_upper = ta.highest(high, length_20) dc_lower = ta.lowest(low, length_20) dc_basis = (dc_upper + dc_lower) / 2 dc_position = (dc_upper - dc_lower) != 0 ? (close - dc_basis) / (dc_upper - dc_lower) : 0 // 41. Standard Deviation std_dev = ta.stdev(close, length_20) std_dev_pct = (std_dev / close) * 100 // 42. Relative Volatility Index (RVI) rvi_up = ta.stdev(close > close ? close : 0, length_14) rvi_down = ta.stdev(close < close ? close : 0, length_14) rvi_total = rvi_up + rvi_down rvi_volatility = rvi_total != 0 ? (rvi_up / rvi_total) * 100 : 50 // 43. Historical Volatility hv_returns = math.log(close / close ) hv = ta.stdev(hv_returns, length_20) * math.sqrt(252) * 100 // 44. Garman-Klass Volatility gk_vol = math.log(high/low) * math.log(high/low) - (2*math.log(2)-1) * math.log(close/open) * math.log(close/open) gk_volatility = math.sqrt(ta.sma(gk_vol, length_20)) * 100 // 45. Parkinson Volatility park_vol = math.log(high/low) * math.log(high/low) parkinson = math.sqrt(ta.sma(park_vol, length_20) / (4 * math.log(2))) * 100 // 46. Rogers-Satchell Volatility rs_vol = math.log(high/close) * math.log(high/open) + math.log(low/close) * math.log(low/open) rogers_satchell = math.sqrt(ta.sma(rs_vol, length_20)) * 100 // |----- OSCILLATOR INDICATORS (5 indicators) -----| // // 47. Elder Ray Index elder_bull = high - ta.ema(close, 13) elder_bear = low - ta.ema(close, 13) elder_power = elder_bull + elder_bear // 48. Schaff Trend Cycle (STC) stc_macd = ta.ema(close, 23) - ta.ema(close, 50) stc_k = ta.stoch(stc_macd, stc_macd, stc_macd, 10) stc_d = ta.ema(stc_k, 3) stc = ta.stoch(stc_d, stc_d, stc_d, 10) // 49. Coppock Curve coppock_roc1 = ta.roc(close, 14) coppock_roc2 = ta.roc(close, 11) coppock = ta.wma(coppock_roc1 + coppock_roc2, 10) // 50. Know Sure Thing (KST) kst_roc1 = ta.roc(close, 10) kst_roc2 = ta.roc(close, 15) kst_roc3 = ta.roc(close, 20) kst_roc4 = ta.roc(close, 30) kst = ta.sma(kst_roc1, 10) + 2*ta.sma(kst_roc2, 10) + 3*ta.sma(kst_roc3, 10) + 4*ta.sma(kst_roc4, 15) // 51. Percentage Price Oscillator (PPO) ppo_line = ((ta.ema(close, 12) - ta.ema(close, 26)) / ta.ema(close, 26)) * 100 ppo_signal = ta.ema(ppo_line, 9) ppo_histogram = ppo_line - ppo_signal // |----- PLOT MAIN INDICATORS -----| // // Plot key momentum indicators plot(rsi_centered, title="01_RSI_Centered", color=color.purple, linewidth=1) plot(stoch_centered, title="02_Stoch_Centered", color=color.blue, linewidth=1) plot(williams_r, title="03_Williams_R", color=color.red, linewidth=1) plot(macd_histogram, title="04_MACD_Histogram", color=color.orange, linewidth=1) plot(cci, title="05_CCI", color=color.green, linewidth=1) // Plot trend indicators plot(sma_momentum, title="06_SMA_Momentum", color=color.navy, linewidth=1) plot(ema_momentum, title="07_EMA_Momentum", color=color.maroon, linewidth=1) plot(sar_trend, title="08_SAR_Trend", color=color.teal, linewidth=1) plot(lr_slope, title="09_LR_Slope", color=color.lime, linewidth=1) plot(mac, title="10_MAC", color=color.fuchsia, linewidth=1) // Plot volatility indicators plot(atr_pct, title="11_ATR_Pct", color=color.yellow, linewidth=1) plot(bb_position, title="12_BB_Position", color=color.aqua, linewidth=1) plot(kc_position, title="13_KC_Position", color=color.olive, linewidth=1) plot(std_dev_pct, title="14_StdDev_Pct", color=color.silver, linewidth=1) plot(bb_width, title="15_BB_Width", color=color.gray, linewidth=1) // Plot volume indicators plot(vroc, title="16_VROC", color=color.blue, linewidth=1) plot(eom_sma, title="17_EOM", color=color.red, linewidth=1) plot(vol_osc, title="18_Vol_Osc", color=color.green, linewidth=1) plot(force_index_sma, title="19_Force_Index", color=color.orange, linewidth=1) plot(obv, title="20_OBV", color=color.purple, linewidth=1) // Plot additional oscillators plot(ao, title="21_Awesome_Osc", color=color.navy, linewidth=1) plot(cmo, title="22_CMO", color=color.maroon, linewidth=1) plot(dpo, title="23_DPO", color=color.teal, linewidth=1) plot(trix, title="24_TRIX", color=color.lime, linewidth=1) plot(fisher, title="25_Fisher", color=color.fuchsia, linewidth=1) // Plot more momentum indicators plot(mfi_centered, title="26_MFI_Centered", color=color.yellow, linewidth=1) plot(ac, title="27_AC", color=color.aqua, linewidth=1) plot(ppo_pct, title="28_PPO_Pct", color=color.olive, linewidth=1) plot(stoch_rsi_centered, title="29_StochRSI_Centered", color=color.silver, linewidth=1) plot(klinger, title="30_Klinger", color=color.gray, linewidth=1) // Plot trend continuation plot(tii, title="31_TII", color=color.blue, linewidth=1) plot(ichimoku_signal, title="32_Ichimoku_Signal", color=color.red, linewidth=1) plot(mama_momentum, title="33_MAMA_Momentum", color=color.green, linewidth=1) plot(zlema_momentum, title="34_ZLEMA_Momentum", color=color.orange, linewidth=1) plot(bop, title="35_BOP", color=color.purple, linewidth=1) // Plot volume continuation plot(nvi, title="36_NVI", color=color.navy, linewidth=1) plot(pvi, title="37_PVI", color=color.maroon, linewidth=1) plot(momentum_pct, title="38_Momentum_Pct", color=color.teal, linewidth=1) plot(roc, title="39_ROC", color=color.lime, linewidth=1) plot(rvi, title="40_RVI", color=color.fuchsia, linewidth=1) // Plot volatility continuation plot(dc_position, title="41_DC_Position", color=color.yellow, linewidth=1) plot(rvi_volatility, title="42_RVI_Volatility", color=color.aqua, linewidth=1) plot(hv, title="43_Historical_Vol", color=color.olive, linewidth=1) plot(gk_volatility, title="44_GK_Volatility", color=color.silver, linewidth=1) plot(parkinson, title="45_Parkinson_Vol", color=color.gray, linewidth=1) // Plot final oscillators plot(rogers_satchell, title="46_RS_Volatility", color=color.blue, linewidth=1) plot(elder_power, title="47_Elder_Power", color=color.red, linewidth=1) plot(stc, title="48_STC", color=color.green, linewidth=1) plot(coppock, title="49_Coppock", color=color.orange, linewidth=1) plot(kst, title="50_KST", color=color.purple, linewidth=1) // Plot final indicators plot(ppo_histogram, title="51_PPO_Histogram", color=color.navy, linewidth=1) plot(pvt, title="52_PVT", color=color.maroon, linewidth=1) // |----- Reference Lines -----| // hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed, linewidth=1) hline(50, "Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1) hline(-50, "Lower Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1) hline(25, "Upper Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1) hline(-25, "Lower Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1) // |----- Enhanced Information Table -----| // if show_table and barstate.islast table_position = position.top_right table_text_size = table_size == "Tiny" ? size.tiny : table_size == "Small" ? size.small : size.normal var table info_table = table.new(table_position, 3, 18, bgcolor=color.new(color.white, 85), border_width=1, border_color=color.gray) // Headers table.cell(info_table, 0, 0, 'Category', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70)) table.cell(info_table, 1, 0, 'Indicator', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70)) table.cell(info_table, 2, 0, 'Value', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70)) // Key Momentum Indicators table.cell(info_table, 0, 1, 'MOMENTUM', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90)) table.cell(info_table, 1, 1, 'RSI Centered', text_color=color.purple, text_size=table_text_size) table.cell(info_table, 2, 1, str.tostring(rsi_centered, '0.00'), text_color=color.purple, text_size=table_text_size) table.cell(info_table, 0, 2, '', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 1, 2, 'Stoch Centered', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 2, 2, str.tostring(stoch_centered, '0.00'), text_color=color.blue, text_size=table_text_size) table.cell(info_table, 0, 3, '', text_color=color.red, text_size=table_text_size) table.cell(info_table, 1, 3, 'Williams %R', text_color=color.red, text_size=table_text_size) table.cell(info_table, 2, 3, str.tostring(williams_r, '0.00'), text_color=color.red, text_size=table_text_size) table.cell(info_table, 0, 4, '', text_color=color.orange, text_size=table_text_size) table.cell(info_table, 1, 4, 'MACD Histogram', text_color=color.orange, text_size=table_text_size) table.cell(info_table, 2, 4, str.tostring(macd_histogram, '0.000'), text_color=color.orange, text_size=table_text_size) table.cell(info_table, 0, 5, '', text_color=color.green, text_size=table_text_size) table.cell(info_table, 1, 5, 'CCI', text_color=color.green, text_size=table_text_size) table.cell(info_table, 2, 5, str.tostring(cci, '0.00'), text_color=color.green, text_size=table_text_size) // Key Trend Indicators table.cell(info_table, 0, 6, 'TREND', text_color=color.navy, text_size=table_text_size, bgcolor=color.new(color.navy, 90)) table.cell(info_table, 1, 6, 'SMA Momentum %', text_color=color.navy, text_size=table_text_size) table.cell(info_table, 2, 6, str.tostring(sma_momentum, '0.00'), text_color=color.navy, text_size=table_text_size) table.cell(info_table, 0, 7, '', text_color=color.maroon, text_size=table_text_size) table.cell(info_table, 1, 7, 'EMA Momentum %', text_color=color.maroon, text_size=table_text_size) table.cell(info_table, 2, 7, str.tostring(ema_momentum, '0.00'), text_color=color.maroon, text_size=table_text_size) table.cell(info_table, 0, 8, '', text_color=color.teal, text_size=table_text_size) table.cell(info_table, 1, 8, 'SAR Trend', text_color=color.teal, text_size=table_text_size) table.cell(info_table, 2, 8, str.tostring(sar_trend, '0'), text_color=color.teal, text_size=table_text_size) table.cell(info_table, 0, 9, '', text_color=color.lime, text_size=table_text_size) table.cell(info_table, 1, 9, 'Linear Regression', text_color=color.lime, text_size=table_text_size) table.cell(info_table, 2, 9, str.tostring(lr_slope, '0.000'), text_color=color.lime, text_size=table_text_size) // Key Volatility Indicators table.cell(info_table, 0, 10, 'VOLATILITY', text_color=color.yellow, text_size=table_text_size, bgcolor=color.new(color.yellow, 90)) table.cell(info_table, 1, 10, 'ATR %', text_color=color.yellow, text_size=table_text_size) table.cell(info_table, 2, 10, str.tostring(atr_pct, '0.00'), text_color=color.yellow, text_size=table_text_size) table.cell(info_table, 0, 11, '', text_color=color.aqua, text_size=table_text_size) table.cell(info_table, 1, 11, 'BB Position', text_color=color.aqua, text_size=table_text_size) table.cell(info_table, 2, 11, str.tostring(bb_position, '0.00'), text_color=color.aqua, text_size=table_text_size) table.cell(info_table, 0, 12, '', text_color=color.olive, text_size=table_text_size) table.cell(info_table, 1, 12, 'KC Position', text_color=color.olive, text_size=table_text_size) table.cell(info_table, 2, 12, str.tostring(kc_position, '0.00'), text_color=color.olive, text_size=table_text_size) // Key Volume Indicators table.cell(info_table, 0, 13, 'VOLUME', text_color=color.blue, text_size=table_text_size, bgcolor=color.new(color.blue, 90)) table.cell(info_table, 1, 13, 'Volume ROC', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 2, 13, str.tostring(vroc, '0.00'), text_color=color.blue, text_size=table_text_size) table.cell(info_table, 0, 14, '', text_color=color.red, text_size=table_text_size) table.cell(info_table, 1, 14, 'EOM', text_color=color.red, text_size=table_text_size) table.cell(info_table, 2, 14, str.tostring(eom_sma, '0.000'), text_color=color.red, text_size=table_text_size) // Key Oscillators table.cell(info_table, 0, 15, 'OSCILLATORS', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90)) table.cell(info_table, 1, 15, 'Awesome Osc', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 2, 15, str.tostring(ao, '0.000'), text_color=color.blue, text_size=table_text_size) table.cell(info_table, 0, 16, '', text_color=color.red, text_size=table_text_size) table.cell(info_table, 1, 16, 'Fisher Transform', text_color=color.red, text_size=table_text_size) table.cell(info_table, 2, 16, str.tostring(fisher, '0.000'), text_color=color.red, text_size=table_text_size) // Summary Statistics table.cell(info_table, 0, 17, 'SUMMARY', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.gray, 70)) table.cell(info_table, 1, 17, 'Total Indicators: 52', text_color=color.black, text_size=table_text_size) regime_color = rsi_centered > 10 ? color.green : rsi_centered < -10 ? color.red : color.gray regime_text = rsi_centered > 10 ? "BULLISH" : rsi_centered < -10 ? "BEARISH" : "NEUTRAL" table.cell(info_table, 2, 17, regime_text, text_color=regime_color, text_size=table_text_size) This makes it the perfect “indicator backbone” for quantitative and systematic traders who want to prototype, combine, and test new regime detection models—especially in combination with the Markov Chain indicator. How to use this script with the Markov Chain for research and backtesting: Add the Enhanced Indicator Export to your chart. Every calculated indicator is available as an individual data stream. Connect the indicator(s) you want as custom input(s) to the Markov Chain’s “Custom Indicators” option. In the Markov Chain indicator’s settings, turn ON the custom indicator mode. For each of the three custom indicator inputs, select the exported plot from the Enhanced Export script—the menu lists all 45+ signals by name. This creates a powerful, modular regime-detection engine where you can mix-and-match momentum, trend, volume, or custom combinations for advanced filtering. Backtest regime logic directly. Once you’ve connected your chosen indicators, the Markov Chain script performs regime detection (Bull/Neutral/Bear) based on your selected features—not just price returns. The regime detection is robust, automatically normalized (using Z-score), and outputs bias (1, -1, 0) for plug-and-play integration. Export the regime bias for programmatic use. As described above, use input.source() in your Pine Script strategy or system and link the bias output. You can now filter signals, control trade direction/size, or design pairs-trading that respect true, indicator-driven market regimes. With this framework, you’re not limited to static or simplistic regime filters. You can rigorously define, test, and refine what “market regime” means for your strategies—using the technical features that matter most to you. Optimize your signal generation by backtesting across a universe of meaningful indicator blends. Enhance risk management with objective, real-time regime boundaries. Accelerate your research: iterate quickly, swap indicator components, and see results with minimal code changes. Automate multi-asset or pairs-trading by integrating regime context directly into strategy logic. Add both scripts to your chart, connect your preferred features, and start investigating your best regime-based trades—entirely within the TradingView ecosystem. References & Further Reading Ang, A., & Bekaert, G. (2002). “Regime Switches in Interest Rates.” Journal of Business & Economic Statistics, 20(2), 163–182. Hamilton, J. D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57(2), 357–384. Markov, A. A. (1906). "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain." The Notes of the Imperial Academy of Sciences of St. Petersburg. Guidolin, M., & Timmermann, A. (2007). “Asset Allocation under Multivariate Regime Switching.” Journal of Economic Dynamics and Control, 31(11), 3503–3544. Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance. Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764. Zucchini, W., MacDonald, I. L., & Langrock, R. (2017). Hidden Markov Models for Time Series: An Introduction Using R (2nd ed.). Chapman and Hall/CRC. On Quantitative Finance and Markov Models: Lo, A. W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. Bloomberg Press. Patterson, S. (2016). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Penguin Press. TradingView Pine Script Documentation: www.tradingview.com TradingView Blog: “Use an Input From Another Indicator With Your Strategy” www.tradingview.com GeeksforGeeks: “What is the Difference Between Markov Chains and Hidden Markov Models?” www.geeksforgeeks.org What makes this indicator original and unique? - On‑chart, real‑time Markov. The chain is drawn directly on your chart. You see the current regime, its tendency to stay (self‑loop), and the usual next step (arrows) as bars confirm. - Source‑agnostic by design. The engine runs on any series you select via input.source() — price, your own oscillator, a composite score, anything you compute in the script. - Automatic normalization + regime mapping. Different inputs live on different scales. The script standardizes your chosen source and maps it into clear regimes (e.g., Bull / Bear / Neutral) without you micromanaging thresholds each time. - Rolling, bar‑by‑bar learning. Transition tendencies are computed from a rolling window of confirmed bars. What you see is exactly what the market did in that window. - Fast experimentation. Switch the source, adjust the window, and the Markov view updates instantly. It’s a rapid way to test ideas and feel regime persistence/switch behavior. Integrate your own signals (using input.source()) - In settings, choose the Source . This is powered by input.source() . - Feed it price, an indicator you compute inside the script, or a custom composite series. - The script will automatically normalize that series and process it through the Markov engine, mapping it to regimes and updating the on‑chart spheres/arrows in real time. Credits: Deep gratitude to @RicardoSantos for both the foundational Markov chain processing engine and inspiring open-source contributions, which made advanced probabilistic market modeling accessible to the TradingView community. Special thanks to @Alien_Algorithms for the innovative and visually stunning 3D sphere logic that powers the indicator’s animated, regime-based visualization. Disclaimer This tool summarizes recent behavior. It is not financial advice and not a guarantee of future results.
Indicatore Pine Script®
di Fractalyst
5
MultiMA fxG v2 Indicateur permettant de centralier 3 moving average : - Moving average Simple 8 (bleu) - Moving average Exponentielle 21 (rouge) - Moving average Exponentielle 50 (Orange) ==================================================== Simple Moving Average (SMA) 8: Displayed in blue, this line provides a quick view of short-term price trends. Exponential Moving Average (EMA) 21: Shown in red, this average is more sensitive to recent price changes and highlights medium-term momentum. Exponential Moving Average (EMA) 50: Marked in orange, this line tracks longer-term price movements for overall trend direction. Traders can use the combination of these moving averages to identify potential crossover signals, trend strength, and possible reversal points.
Indicatore Pine Script®
di badabuXXI